Improper transfer technique predisposes wheelchair users to developing upper arm pain and injuries. A large body of research on the proper ways to transfer to avoid extraneous forces exists but these techniques have not been well disseminated into clinics. In a recent study we found that up to 63% of Veterans who use wheelchairs have many transfer skill deficits including improper arm positioning, use of handgrips, and head- hips relationship. We propose to develop TransKinect, an automated transfer assessment system for clinical settings that can help therapists and their patients to identify improper transfer motions and provide guidance on how to improve their technique. The system will be based on the Transfer Assessment Instrument (TAI) which is a valid and reliable scale used to assess the quality of transfer technique but requires new therapists considerable time to learn and to make judgements on appropriate body positioning and mechanics. TransKinect will eliminate the need for background knowledge and training on the TAI and provide objective measurement of body and joint motions using a portable, low-cost markerless motion capture sensor. In prior work, we developed machine learning classifiers for TransKinect that can differentiate proper from improper techniques with an average accuracy of 94% using the sensor data recorded during a transfer. These classifiers will be embedded in system software designed to `watch' a transfer, automatically compute the TAI score, present the results in real-time and provide education and training recommendations to therapists and their patients. The specific aims are: 1) to iteratively develop the TransKinect software platform involving input from an expert panel, 2) to validate TransKinect for clinical use by comparing the TAI scores generated by the system against those of expert therapists 3) to test the usability of TransKinect with a novice group of therapists and 4) field test the system in a VA clinical setting to examine its utility, usability and effectiveness. TransKinect will be iteratively refined using the data that is collected in each aim of the study. For Aim 1, a prototype of TransKinect will be developed and tested with field experts familiar with the TAI. For Aim 2 three of the expert therapists and TransKinect will score the TAI after watching 30 Veterans perform wheelchair transfers. We aim to achieve at least 80% of agreement between the two sets of scores and will refine the software using data collected in this aim if necessary. For Aim 3, 10 novice therapists wlll use TransKinect to assess a model patient and provide their feedback on the usability and perceived usefulness of the system. We hypothesize that the therapists will find TransKinect easy to setup and use and that it increases their awareness and understanding about transfer technique. In Aim 4 the TransKinect system will be introduced into clinical practice at the Clement J. Zablocki VA Medical Center where we will track the system's utility, usability and effectiveness. Effectiveness will be evaluated by comparing TAI scores on patients who were assessed at an initial and followup visit to the clinic. We hope to show that the system can be successfully integrated into standard practice and is sensitive to detecting improvements in transfer technique.